ArticleCommunications engineering2025
Validation of an electroencephalography data assimilation-based computational approach for estimating cortical excitation-inhibition balance.
Article in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Concurrent Multimodal Imaging Demonstrates That EEG-Based Excitation/Inhibition Balance Reflects Glutamate and GABA Balance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Validation of an electroencephalography data assimilation-based computational approach for estimating cortical excitation-inhibition balance.Communications engineering · 2025Article
- Digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data.NPJ digital medicine · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The excitation and inhibition (E/I) balance of neural circuits is a crucial index of neurophysiological homeostasis associated with healthy brain functioning. Although several cutting-edge methods exist to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To address this, we introduced neural-mass-model-based tracking using a data assimilation (DA) approach. While we previously demonstrated that sleep-dependent E/I changes could be estimated from electroencephalography (EEG) data, the neurophysiological validity of this method had not been directly evaluated. In this study, we developed an enhanced DA-based method and compared its E/I estimates with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based measures. Our results revealed significant correlations between the DA-based estimates and TMS-EEG indices of E/I balance in the dorsolateral prefrontal cortex. These findings indicate that our computational approach provides neurophysiologically valid estimations of time-varying E/I balance.
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Registered trials
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